Eigenvalue-based Cyclostationary Spectrum Sensing Using Multiple Antennas

In this paper, we propose a signal-selective spectrum sensing method for cognitive radio networks and specifically targeted for receivers with multiple-antenna capability. This method is used for detecting the presence or absence of primary users based on the eigenvalues of the cyclic covariance matrix of received signals. In particular, the cyclic correlation significance test is used to detect a specific signal-of-interest by exploiting knowledge of its cyclic frequencies. The analytical threshold for achieving constant false alarm rate using this detection method is presented, verified through simulations, and shown to be independent of both the number of samples used and the noise variance, effectively eliminating the dependence on accurate noise estimation. The proposed method is also shown, through numerical simulations, to outperform existing multiple-antenna cyclostationary-based spectrum sensing algorithms under a quasi-static Rayleigh fading channel, in both spatially correlated and uncorrelated noise environments. The algorithm also has significantly lower computational complexity than these other approaches.Comment: 6 pages, 6 figures, accepted to IEEE GLOBECOM 201

Multiscale analysis of high frequency exchange rate time series

Imperial Users onl

FPGA implementation of a cyclostationary detector for OFDM signals

Due to the ubiquity of Orthogonal Frequency Division Multiplexing (OFDM) based communications standards such as IEEE 802.11 a/g/n and 3GPP Long Term Evolution (LTE), a growing interest has developed in techniques for reliably detecting the presence of these signals in dynamic radio systems. A popular approach for detection is to exploit the cyclostationary nature of OFDM communications signals. In this paper, we focus on a frequency domain cyclostationary detection algorithm first introduced by Giannakis and Dandawate and study its performance in detecting IEEE 802.11a OFDM signals in the presence of practical radio impairments such as Carrier Frequency offset (CFO), Phase Noise, I/Q Imbalance, Multipath Fading and DC offset. We then present a hardware implementation of this algorithm developed using MathWorks HDL Coder and provide implementation results after targeting to a Xilinx 7 Series FPGA device

Identifying Elderly Patients at Risk of Falling using Time-Domain and Cyclostationarity Related Features

Falls are a prevalent and severe health problem in the elderly community, leading to unfortunate and devastating consequences. Some falls can be prevented through interventions, proper management, and extra care. Therefore, studying and identifying elderly people with risk of falls is essential to minimize the falling risk and to minimize the severity of injuries that can occur from these falls. Besides, identifying at-risk patients can profoundly affect public health in a positive way. In this paper, we use classification techniques to identify at-risk patients using pressure signals of the innersoles of 520 elderly people. These people reported whether they had experienced previous falls or not. Two different types of feature sets were used as inputs to the classification models and were compared: The first feature set includes time-domain, physiological, and cyclostationary features, whereas the second includes a subset of those features chosen by Relief-F as the most important features. Our study showed that the use of features from different walking conditions and using Relief-F as a feature selection method significantly improved the model prediction accuracy, i.e. by 5.24% from the best previously existing model. The results also point out that the mean and standard deviation of the stride time, gender, the degree of cyclostationarity were the most important features to include in classification models for the identification of elderly people at risk of falling

Blind Estimation of OFDM System Parameters for Automatic Signal Identification

Orthogonal frequency division multiplexing (OFDM) has gained worldwide popular­ ity in broadband wireless communications recently due to its high spectral efficiency and robust performance in multipath fading channels. A growing trend of smart receivers which can support and adapt to multiple OFDM based standards auto­ matically brings the necessity of identifying different standards by estimating OFDM system parameters without a priori information. Consequently, blind estimation and identification of OFDM system parameters has received considerable research atten­ tions. Many techniques have been developed for blind estimation of various OFDM parameters, whereas estimation of the sampling frequency is often ignored. Further­ more, the estimated sampling frequency of an OFDM signal has to be very accurate for data recovery due to the high sensitivity of OFDM signals to sampling clock offset. To address the aforementioned problems, we propose a two-step cyclostation- arity based algorithm with low computational complexity to precisely estimate the sampling frequency of a received oversampled OFDM signal. With this estimated sampling frequency and oversampling ratio, other OFDM system parameters, i.e., the number of subcarriers, symbol duration and cyclic prefix (CP) length can be es­ timated based on the cyclic property from CP sequentially. In addition, modulation scheme used in the OFDM can be classified based on the higher-order statistics (HOS) of the frequency domain OFDM signal. All the proposed algorithms are verified by a lab testing system including a vec­ tor signal generator, a spectrum analyzer and a high speed digitizer. The evaluation results confirm the high precision and efficacy of the proposed algorithm in realistic scenarios